The advent of global communications networks such as the Internet has presented commercial opportunities for reaching vast numbers of potential customers. Electronic messaging, and particularly electronic mail (“email”), is becoming increasingly pervasive as a means for disseminating unwanted advertisements and promotions (also denoted as “spam”) to network users.
The Radicati Group, Inc., a consulting and market research firm, estimates that as of August 2002, two billion junk e-mail messages are sent each day—this number is expected to triple every two years. Individuals and entities (e.g., businesses, government agencies) are becoming increasingly inconvenienced and oftentimes offended by junk messages. As such, spam is now or soon will become a major threat to trustworthy computing.
A key technique utilized to thwart spam involves the employment of filtering systems/methodologies. One proven filtering technique is based upon a machine learning approach—machine learning filters assign to an incoming message a probability that the message is spam. In this approach, features typically are extracted from two classes of example messages (e.g., spam and non-spam messages), and a learning filter is applied to discriminate probabilistically between the two classes. Since many message features are related to content (e.g., words and phrases in the subject and/or body of the message), such types of filters are commonly referred to as “content-based filters”.
Despite the onslaught of such spam filtering techniques, many spammers have thought of ways to disguise their identities to avoid and/or bypass spam filters. Thus, conventional content-based and adaptive filters may become ineffective in recognizing and blocking disguised spam messages.
Instead of focusing on the recipient-end of spam, recent developments in anti-spam technology have concentrated on minimizing spammers' resources or their ability to send spam. For example, much of the current research involves inhibiting access to free email accounts from which massive amounts of spam can be sent. In particular, service providers have begun to require that potential account holders solve computational puzzles and/or human interactive proofs (HIPs) in order to obtain email accounts. Because computational puzzles and HIPs are designed to be too difficult for computers to solve but easy enough for humans, they tend to at least hinder new account sign-ups en mass which is typically performed by computers.
Computational puzzles have also been extended to individual senders. For example, recipients can require a sender to correctly solve a puzzle if they suspect the sender is a spammer. Theoretically, this practice is effective at catching disguised spammers; but unfortunately, it is not a foolproof tactic against their ever-adapting nature.